Deadline Radar AI

✅ Inspiration

College students often struggle not because of lack of ability, but because they miss important deadlines such as assignments, exam registrations, fee payments, and internship applications. Existing productivity apps require manual task entry and do not understand a student’s academic lifecycle.

We wanted to build a system that predicts deadlines instead of just reminding them, helping students stay ahead and reduce stress. This inspired us to create Deadline Radar AI, a smart assistant that transforms deadline management into an intelligent, automated experience.

✅ What it does

Deadline Radar AI is an AI-powered student life intelligence system that predicts, tracks, and analyzes important academic, financial, and career deadlines.

The application:

Automatically generates academic deadlines based on semester start date

Categorizes deadlines into academic, financial, career, and personal

Calculates a risk score to detect deadline overload

Provides AI-based recommendations to improve planning

Tracks expenses and predicts budget overspending

Displays analytics dashboards showing deadline trends and risk levels

Instead of acting like a simple to-do list, the system works as a predictive deadline radar for students.

✅ How we built it

We built Deadline Radar AI using the Mendix low-code platform (Medo).

Key implementation steps:

Designed data models for users, deadlines, expenses, and risk scores

Used microflows to automatically generate deadlines dynamically

Implemented calculated attributes for urgency and overload risk scoring

Created analytics dashboards using charts and visual indicators

Built rule-based AI logic to generate smart recommendations

Developed a clean, mobile-responsive interface for easy usability

The platform allowed rapid development while focusing on logic, analytics, and user experience.

✅ Challenges we ran into

Designing a meaningful risk scoring algorithm that accurately reflects deadline pressure

Converting real academic timelines into automated logic

Balancing simplicity for beginners while keeping the system intelligent

Ensuring dashboards clearly communicated analytics without overwhelming users

Structuring AI recommendations using rule-based logic instead of complex machine learning

These challenges helped us refine both the technical design and user experience.

✅ Accomplishments that we're proud of

Successfully created a predictive system, not just a reminder app

Built automated deadline generation using logical workflows

Implemented analytics-driven decision support for students

Designed a practical solution addressing real student problems

Delivered a functional AI-inspired application within a hackathon timeframe

We are especially proud of transforming a common problem into an intelligent, scalable solution.

✅ What we learned

Through this project, we learned:

How low-code platforms can build powerful applications quickly

The importance of data-driven decision systems

Designing user-centric solutions based on real-world problems

Implementing analytics and automation using microflows

Translating AI concepts into practical rule-based systems

We also learned how structured problem-solving improves both innovation and usability.

✅ What's next for Deadline Radar AI

Future improvements include:

Integration with college academic calendars automatically

Real AI/ML models for personalized predictions

Notification system with smart alerts

Collaboration features for group projects

Mobile app deployment

Integration with learning platforms and internship portals

Our long-term vision is to evolve Deadline Radar AI into a complete digital operating system for student life management.

Built With

• ai
• react
• supabase
• tailwindcss
• typescript